S. Se, D. Lowe, and J. Little. Local and Global Localization for Mobile Robots using Visual Landmarks. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '01), Hawaii, USA, October 2001. Home/Search Document Details and Download
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Mobile Robot Localization and Mapping with Uncertainty Using .. - Se, Lowe, Little (2002) (9 citations) Self-citation (Se Lowe Little) (Correct)
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S. Se, D. Lowe, and J. Little. Local and global localization for mobile robots using visual landmarks. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 414--420, Maui, Hawaii, October 2001.
Vision-based Mapping with Backward Correction - Se, Lowe, Little (2002) Self-citation (Se Lowe Little) (Correct)
....due to slippage occurrences. a) Submap 1. b) Submap 2. c) Submap 3. d) Submap 4. a nearby position. This allows the detection of drift occurrences. To cater for the e#ect of drift in map building, we can estimate the actual robot pose based on the current frame, as in global localization [9], to correct the odometry for subsequent frames. However, we employ an alternative method which starts building a new map whenever a drift is detected. Afterwards, all the submaps are aligned and combined to obtain a complete global map. This approach is more robust as the drift estimation is ....
S. Se, D. Lowe, and J. Little. Local and global localization for mobile robots using visual landmarks. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 414--420, Maui, Hawaii, October 2001.
Global Localization Using Distinctive Visual Features - Se, Lowe, Little (2002) (3 citations) Self-citation (Se Lowe Little) (Correct)
....of landmarks into close alignment, treating global localization as a search problem. The Hough Transform [7] with a three dimensional discretized search space (X, Z, #) is used, where X is the sideways translation, Z is the forward translation and # is the orientation. The algorithm is as follows [11]: For each SIFT feature in the current frame, find the set of N potential SIFT landmarks in the 227 database that match, using the local image vector and the height as the preliminary constraints. For each of the potential matches, compute all the possible poses and vote the corresponding ....
....frame is used for global localization here, there are insu#cient matches in cases M2 and M4 for a reliable estimation. 8 Conclusion In our previous work [12] we have built a database map with distinctive SIFT landmarks. We have developed a Hough Transform approach for global localization in [11]. In this paper, we proposed a RANSAC approach for matching SIFT features in the current frame to the database e#ciently, to localize globally. We have demonstrated that the robot can globally localize itself well using SIFT features, even from just the current frame. The contribution of this ....
S. Se, D. Lowe, and J. Little. Local and global localization for mobile robots using visual landmarks. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 414--420, Maui, Hawaii, October 2001.
Global Localization using Distinctive Visual Features - Se, Lowe, Little (2002) (3 citations) Self-citation (Se Lowe Little) (Correct)
....of landmarks into close alignment, treating global localization as a search problem. The Hough Transform [7] with a three dimensional discretized search space (X, Z, 0) is used, where X is the sideways translation, Z is the forward translation and 0 is the orientation. The algorithm is as follows [11]: For each SIFT feature in the current frame, find the set of N potential SIFT landmarks in the database that match, using the local image vector and the height as the preliminary constraints. For each of the potential matches, compute all the possible poses and vote the corresponding Hough ....
....frame is used for global localization here, there are insufficient matches in cases M2 and M4 for a reliable estimation. 8 Conclusion In our previous work [12] we have built a database map with distinctive SIFT landmarks. We have developed a Hough Transform approach for global localiza tion in [11]. In this paper, we proposed a RANSAC approach for matching SIFT features in the current frame to the database efficiently, to localize globally. We have demonstrated that the robot can globally localize itself well using SIFT features, even from just the current frame. The contribution of this ....
S. Se, D. Lowe, and J. Little. Local and global local- ization for mobile robots using visual landmarks. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 414 420, Maul, Hawaii, October 2001.
Vision-based Mapping with Backward Correction - Se, Lowe, Little (2002) Self-citation (Se Lowe Little) (Correct)
....due to slippage occurrences. a) Submap 1. b) Submap 2. c) Submap 3. d) Submap J. a nearby position. This allows the detection of drift occurrences. To cater for the effect of drift in map building, we can estimate the actual robot pose based on the current frame, as in global localization [9], to correct the odometry for subsequent frames. However, we employ an alternative method which starts building a new map whenever a drift is detected. Afterwards, all the submaps are aligned and combined to obtain a complete global map. This approach is more robust as the drift estimation is ....
S. Se, D. Lowe, and J. Little. Local and global local- ization for mobile robots using visual landmarks. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 414 420, Maui, Hawaii, October 2001.
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Se S, Lowe D, Little J: Local and global localization for mobile robots using visual landmarks. In 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, 414--420, IEEE, Piscataway, 2001.
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